Radiology

Robot-assisted ultrasound probe calibration for image-guided interventions.

BACKGROUND: Trackable ultrasound probes facilitate ultrasound-guided procedures, allowing real-time fusion of augmented ultrasound images and live video streams. The integration aids surgeons in accurately locating lesions within organs, and this could only be achieved through a precise registration between the ultrasound probe and the ultrasound image. Currently, calibration and registration proc...

MetaGP: A generative foundation model integrating electronic health records and multimodal imaging for addressing unmet clinical needs.

Artificial intelligence makes strides in specialized diagnostics but faces challenges in complex clinical scenarios, such as rare disease diagnosis and emergency condition identification. To address these limitations, we develop Meta General Practitioner (MetaGP), a 32-billion-parameter generative foundation model trained on extensive datasets, including over 8 million electronic health records, b...

A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.

Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to ac...

Deep learning prediction of mammographic breast density using screening data.

This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fa...

Method of Forearm Muscles 3D Modeling Using Robotic Ultrasound Scanning.

The accurate assessment of muscle morphology and function is crucial for medical diagnostics, rehabilitation, and biomechanical research. This study p...

Information Extraction from Lumbar Spine MRI Radiology Reports Using GPT4: Accuracy and Benchmarking Against Research-Grade Comprehensive Scoring.

: This study aimed to create a pipeline for standardized data extraction from lumbar-spine MRI radiology reports using a large language model (LLM) an...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progre...

Robust Sensory Information Reconstruction and Classification With Augmented Spikes.

Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibi...

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and re...

Self-Supervised Multi-Scale Multi-Modal Graph Pool Transformer for Sellar Region Tumor Diagnosis.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the se...

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. ...

Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN.

For optical coherence tomography angiography (OCTA) images, the limited scanning rate leads to a trade-off between field-of-view (FOV) and imaging res...

CorrMorph: Unsupervised Deformable Brain MRI Registration Based on Correlation Mining.

Deformable image registration, as a fundamental prerequisite for many medical image analysis tasks, has received considerable attention. However, exis...

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in pr...

A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeo...

Brain tumor segmentation and detection in MRI using convolutional neural networks and VGG16.

BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection sy...

A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.

Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules const...

Radiomics across modalities: a comprehensive review of neurodegenerative diseases.

Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human...

A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.

Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability di...

FET-UNet: Merging CNN and transformer architectures for superior breast ultrasound image segmentation.

PURPOSE: Breast cancer remains a significant cause of mortality among women globally, highlighting the critical need for accurate diagnosis. Although ...